{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    2\n",
       "2    5\n",
       "3    0\n",
       "4    6\n",
       "5    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Serise 对象可以理解为一维数组\n",
    "s = pd.Series([4,2,5,0,6,3])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.238602</td>\n",
       "      <td>0.302857</td>\n",
       "      <td>0.500267</td>\n",
       "      <td>0.501565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.277735</td>\n",
       "      <td>-0.043156</td>\n",
       "      <td>0.525540</td>\n",
       "      <td>-0.714413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.563791</td>\n",
       "      <td>-0.057094</td>\n",
       "      <td>1.141720</td>\n",
       "      <td>2.096407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.480371</td>\n",
       "      <td>0.625397</td>\n",
       "      <td>1.203549</td>\n",
       "      <td>1.315902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.005178</td>\n",
       "      <td>2.362845</td>\n",
       "      <td>-0.087680</td>\n",
       "      <td>0.047992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792063</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>0.271744</td>\n",
       "      <td>-0.085013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.238602  0.302857  0.500267  0.501565\n",
       "1  1.277735 -0.043156  0.525540 -0.714413\n",
       "2 -0.563791 -0.057094  1.141720  2.096407\n",
       "3 -1.480371  0.625397  1.203549  1.315902\n",
       "4 -1.005178  2.362845 -0.087680  0.047992\n",
       "5 -0.792063 -2.150270  0.271744 -0.085013"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame是二位数组对象\n",
    "df = pd.DataFrame(np.random.randn(6,4),columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "索引访问"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.238602\n",
       "B    0.302857\n",
       "C    0.500267\n",
       "D    0.501565\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.238602\n",
       "1    1.277735\n",
       "2   -0.563791\n",
       "3   -1.480371\n",
       "4   -1.005178\n",
       "5   -0.792063\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.238602</td>\n",
       "      <td>0.302857</td>\n",
       "      <td>0.500267</td>\n",
       "      <td>0.501565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.277735</td>\n",
       "      <td>-0.043156</td>\n",
       "      <td>0.525540</td>\n",
       "      <td>-0.714413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.563791</td>\n",
       "      <td>-0.057094</td>\n",
       "      <td>1.141720</td>\n",
       "      <td>2.096407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.238602  0.302857  0.500267  0.501565\n",
       "1  1.277735 -0.043156  0.525540 -0.714413\n",
       "2 -0.563791 -0.057094  1.141720  2.096407"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)#前3行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.005178</td>\n",
       "      <td>2.362845</td>\n",
       "      <td>-0.087680</td>\n",
       "      <td>0.047992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792063</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>0.271744</td>\n",
       "      <td>-0.085013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "4 -1.005178  2.362845 -0.087680  0.047992\n",
       "5 -0.792063 -2.150270  0.271744 -0.085013"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)#后2行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index #访问数据的行索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.467045</td>\n",
       "      <td>0.173430</td>\n",
       "      <td>0.592523</td>\n",
       "      <td>0.527073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.951615</td>\n",
       "      <td>1.450117</td>\n",
       "      <td>0.500708</td>\n",
       "      <td>1.022856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.480371</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>-0.087680</td>\n",
       "      <td>-0.714413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.951899</td>\n",
       "      <td>-0.053610</td>\n",
       "      <td>0.328875</td>\n",
       "      <td>-0.051762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.677927</td>\n",
       "      <td>0.129851</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>0.274779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.319899</td>\n",
       "      <td>0.544762</td>\n",
       "      <td>0.987675</td>\n",
       "      <td>1.112318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.277735</td>\n",
       "      <td>2.362845</td>\n",
       "      <td>1.203549</td>\n",
       "      <td>2.096407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.467045  0.173430  0.592523  0.527073\n",
       "std    0.951615  1.450117  0.500708  1.022856\n",
       "min   -1.480371 -2.150270 -0.087680 -0.714413\n",
       "25%   -0.951899 -0.053610  0.328875 -0.051762\n",
       "50%   -0.677927  0.129851  0.512903  0.274779\n",
       "75%   -0.319899  0.544762  0.987675  1.112318\n",
       "max    1.277735  2.362845  1.203549  2.096407"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() #查看简单的数据统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.501565</td>\n",
       "      <td>0.500267</td>\n",
       "      <td>0.302857</td>\n",
       "      <td>-0.238602</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.714413</td>\n",
       "      <td>0.525540</td>\n",
       "      <td>-0.043156</td>\n",
       "      <td>1.277735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.096407</td>\n",
       "      <td>1.141720</td>\n",
       "      <td>-0.057094</td>\n",
       "      <td>-0.563791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.315902</td>\n",
       "      <td>1.203549</td>\n",
       "      <td>0.625397</td>\n",
       "      <td>-1.480371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.047992</td>\n",
       "      <td>-0.087680</td>\n",
       "      <td>2.362845</td>\n",
       "      <td>-1.005178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.085013</td>\n",
       "      <td>0.271744</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>-0.792063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          D         C         B         A\n",
       "0  0.501565  0.500267  0.302857 -0.238602\n",
       "1 -0.714413  0.525540 -0.043156  1.277735\n",
       "2  2.096407  1.141720 -0.057094 -0.563791\n",
       "3  1.315902  1.203549  0.625397 -1.480371\n",
       "4  0.047992 -0.087680  2.362845 -1.005178\n",
       "5 -0.085013  0.271744 -2.150270 -0.792063"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis = 1,ascending = False) #对索引进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "  <tbody>\n",
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       "      <th>5</th>\n",
       "      <td>-0.792063</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>0.271744</td>\n",
       "      <td>-0.085013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.563791</td>\n",
       "      <td>-0.057094</td>\n",
       "      <td>1.141720</td>\n",
       "      <td>2.096407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.277735</td>\n",
       "      <td>-0.043156</td>\n",
       "      <td>0.525540</td>\n",
       "      <td>-0.714413</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.238602</td>\n",
       "      <td>0.302857</td>\n",
       "      <td>0.500267</td>\n",
       "      <td>0.501565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.480371</td>\n",
       "      <td>0.625397</td>\n",
       "      <td>1.203549</td>\n",
       "      <td>1.315902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.005178</td>\n",
       "      <td>2.362845</td>\n",
       "      <td>-0.087680</td>\n",
       "      <td>0.047992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "5 -0.792063 -2.150270  0.271744 -0.085013\n",
       "2 -0.563791 -0.057094  1.141720  2.096407\n",
       "1  1.277735 -0.043156  0.525540 -0.714413\n",
       "0 -0.238602  0.302857  0.500267  0.501565\n",
       "3 -1.480371  0.625397  1.203549  1.315902\n",
       "4 -1.005178  2.362845 -0.087680  0.047992"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = 'B') #根据B数据从小到大进行排序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据访问"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>-1.480371</td>\n",
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       "      <td>1.203549</td>\n",
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       "      <th>4</th>\n",
       "      <td>-1.005178</td>\n",
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       "          A         B         C         D\n",
       "3 -1.480371  0.625397  1.203549  1.315902\n",
       "4 -1.005178  2.362845 -0.087680  0.047992"
      ]
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[3:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.087680</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792063</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>0.271744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.238602  0.302857  0.500267\n",
       "1  1.277735 -0.043156  0.525540\n",
       "2 -0.563791 -0.057094  1.141720\n",
       "3 -1.480371  0.625397  1.203549\n",
       "4 -1.005178  2.362845 -0.087680\n",
       "5 -0.792063 -2.150270  0.271744"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A','B','C']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4803707174875675"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[3,'A'] #使用DataFrame.loc()函数通过标签选择某个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4803707174875675"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3,0] #使用DataFrame.iloc()函数通过数组索引来访问某个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>-0.043156</td>\n",
       "      <td>0.525540</td>\n",
       "      <td>-0.714413</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.563791</td>\n",
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       "      <td>1.141720</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792063</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>0.271744</td>\n",
       "      <td>-0.085013</td>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.238602  0.302857  0.500267  0.501565\n",
       "1  1.277735 -0.043156  0.525540 -0.714413\n",
       "2 -0.563791 -0.057094  1.141720  2.096407\n",
       "3 -1.480371  0.625397  1.203549  1.315902\n",
       "5 -0.792063 -2.150270  0.271744 -0.085013"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.C > 0] #使用布尔值来选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数据进行修改\n",
    "df['TAG'] = ['cat','dog','cat','cat','cat','dog']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C</th>\n",
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       "      <th>TAG</th>\n",
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       "      <td>0.501565</td>\n",
       "      <td>cat</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.277735</td>\n",
       "      <td>-0.043156</td>\n",
       "      <td>0.525540</td>\n",
       "      <td>-0.714413</td>\n",
       "      <td>dog</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.563791</td>\n",
       "      <td>-0.057094</td>\n",
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       "      <td>2.096407</td>\n",
       "      <td>cat</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.480371</td>\n",
       "      <td>0.625397</td>\n",
       "      <td>1.203549</td>\n",
       "      <td>1.315902</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.005178</td>\n",
       "      <td>2.362845</td>\n",
       "      <td>-0.087680</td>\n",
       "      <td>0.047992</td>\n",
       "      <td>cat</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.792063</td>\n",
       "      <td>-2.150270</td>\n",
       "      <td>0.271744</td>\n",
       "      <td>-0.085013</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D  TAG\n",
       "0 -0.238602  0.302857  0.500267  0.501565  cat\n",
       "1  1.277735 -0.043156  0.525540 -0.714413  dog\n",
       "2 -0.563791 -0.057094  1.141720  2.096407  cat\n",
       "3 -1.480371  0.625397  1.203549  1.315902  cat\n",
       "4 -1.005178  2.362845 -0.087680  0.047992  cat\n",
       "5 -0.792063 -2.150270  0.271744 -0.085013  dog"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAG</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cat</th>\n",
       "      <td>-3.287941</td>\n",
       "      <td>3.234005</td>\n",
       "      <td>2.757855</td>\n",
       "      <td>3.961867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog</th>\n",
       "      <td>0.485672</td>\n",
       "      <td>-2.193426</td>\n",
       "      <td>0.797284</td>\n",
       "      <td>-0.799426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D\n",
       "TAG                                        \n",
       "cat -3.287941  3.234005  2.757855  3.961867\n",
       "dog  0.485672 -2.193426  0.797284 -0.799426"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('TAG').sum()#根据TAG列做分组统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-11', '2018-11-12', '2018-11-13', '2018-11-14',\n",
       "               '2018-11-15', '2018-11-16', '2018-11-17', '2018-11-18',\n",
       "               '2018-11-19', '2018-11-20', '2018-11-21', '2018-11-22',\n",
       "               '2018-11-23', '2018-11-24', '2018-11-25', '2018-11-26',\n",
       "               '2018-11-27', '2018-11-28', '2018-11-29', '2018-11-30'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('20181111',periods=20)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.924712\n",
       "2000-01-02   -0.974606\n",
       "2000-01-03   -0.741436\n",
       "2000-01-04    0.544994\n",
       "2000-01-05    0.300018\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#时间序列\n",
    "n_items = 366\n",
    "ts = pd.Series(np.random.randn(n_items),index=pd.date_range('20000101',periods=n_items))\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31     4.355376\n",
       "2000-02-29     8.316687\n",
       "2000-03-31     0.607147\n",
       "2000-04-30    -8.088090\n",
       "2000-05-31    11.382918\n",
       "2000-06-30    -2.496197\n",
       "2000-07-31    -4.248403\n",
       "2000-08-31   -13.026115\n",
       "2000-09-30   -16.145823\n",
       "2000-10-31     9.230099\n",
       "2000-11-30    -3.647965\n",
       "2000-12-31    11.121963\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照月份聚合\n",
    "ts.resample('1m').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x130ac6aeba8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据可视化\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = (10,6),dpi = 144)\n",
    "cs = ts.cumsum()\n",
    "cs.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x130acb625f8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6),dpi = 144)\n",
    "ts.resample('1m').sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
